Forthcoming Articles

International Journal of Advanced Mechatronic Systems

International Journal of Advanced Mechatronic Systems (IJAMechS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Advanced Mechatronic Systems (2 papers in press)

Regular Issues

  • Resilient fuzzy SVM control design for nonlinear Markov jump cyber physical systems against replay attacks   Order a copy of this article
    by Maryam Fattahi 
    Abstract: This paper presents a novel control strategy framework using fuzzy support vector machine (FSVM) for enhancing the detection and resilience of nonlinear Markov jump cyber-physical systems (NMJCPS) under replay attacks. At first, an introduction to FSVM method is given. Then, dynamic of MJCPS with replay attack is presented. In such condition, resilient FSVM control mechanism for stabilisation of system in condition of abnormal behaviour due to replay attacks is proposed. The proposed new approach ensures system stability and performance during and after the attack. Simulation results demonstrate the effectiveness of the control design in mitigating the effects of replay attacks and maintaining system functionality.
    Keywords: fuzzy SVM control; nonlinear Markov jump cyber-physical systems; NMJCPS; replay attacks.
    DOI: 10.1504/IJAMECHS.2026.10075373
     
  • Atransformer-based model of fine-grained image classification for cigarette trademark identification   Order a copy of this article
    by Xiaohui Li, Haoran Zhu, Yupeng Xu, Changshen Yan, Lan Yao, Feng Zeng 
    Abstract: Traditional methods in fine-grained image recognition for cigarette trademarks have a limitation of low accuracy. In this paper, we propose a fine-grained image classification model with a transformer-based network architecture for cigarette images. In the proposed model, the local feature refinement module focuses on the critical areas of cigarette trademarks and packs via channel and spatial attention mechanisms, while the adaptive feature fusion module integrates multiscale features to enhance classification accuracy. Additionally, we design a novel loss function based on weighted cross-entropy and region sensitivity, allowing the model to focus on both global and local fine-grained features. Experimental results demonstrate that our proposed model achieves an accuracy of 99.5% on the Furongwang dataset, 98.1% on the Yuxi dataset, and 98.8% on the Liqun dataset, surpassing the best-performing existing method by up to 1.1%. These results confirm the effectiveness of our approach in the fine-grained classification of cigarette trademarks.
    Keywords: fine-grained image recognition; transformer; attention mechanism; feature fusion.
    DOI: 10.1504/IJAMECHS.2026.10076583
     

 

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